Anomalous Water Vapor Circulation in an Extreme Drought Event of the Mid‐Reaches of the Lancang‐Mekong River Basin
Bibliographic record
Abstract
Abstract The middle reaches of the Lancang‐Mekong River Basin (M‐LMRB) experienced a record‐breaking drought event in 2019, resulting in significant economic losses of approximately 650 million dollars and affecting a population of 17 million. However, the anomalous circulation and transportation processes of water vapor, which may have played a crucial role in inducing the extreme drought, have not been fully studied. In this study, we analyze the water vapor circulation during the 2019 drought event using the land‐atmosphere water balance and a backward trajectory model for moisture tracking. Our results indicate that the precipitation in the M‐LMRB from May to October 2019 was only 71.9% of the long‐term climatological mean (1959–2021). The low precipitation during this drought event can be attributed to less‐than‐normal external water vapor supply. Specifically, the backward trajectory model reveals a decrease in the amount of water vapor transported from the Indian Ocean, the Bay of Bengal, and the Pacific Ocean, which are the main moisture sources for precipitation in the region. Comparing the atmospheric circulation patterns in 2019 with the climatology, we identify anomalous anticyclone conditions in the Bay of Bengal, anomalous westerlies in the Northeast Indian Ocean, and an anomalous cyclone in the Western Pacific Ocean, collectively facilitating a stronger export of water vapor from the region. Therefore, the dynamic processes played a more significant role than thermodynamic processes in contributing to the 2019 extreme drought event.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".